AlphaAgents: A Multi-Agent Stock Portfolio Construction System Based on Large Language Models

AlphaAgents is an open-source implementation that combines large language models (LLMs) with a multi-agent architecture for automated stock portfolio construction and optimization. It simulates the professional division of labor in an investment team, executing end-to-end tasks from market analysis to asset allocation, with the goal of capturing excess returns (Alpha) and providing innovative solutions for complex financial decisions.

The design of AlphaAgents is inspired by the operational model of modern asset management companies. Unlike traditional quantitative systems that rely on hard-coded algorithms, this project uses LLMs to drive decision-making for various professional agents. The term "Alpha" in the name refers to excess returns that outperform market benchmarks. The system aims to capture non-linear relationships and qualitative factors that are difficult to identify with traditional models, requiring deep reasoning and cross-domain knowledge integration.

AlphaAgents consists of multiple specialized agents:

• Market Analysis Agent: Monitors market dynamics, news, and macroeconomic indicators, extracts investment signals from unstructured text, and understands semantics and market sentiment;
• Stock Selection Strategy Agent: Selects targets by combining financial indicators (P/E ratio, ROE, etc.) with qualitative factors (corporate governance, industry prospects);
• Asset Allocation Agent: Optimizes weights for multiple objectives (maximizing returns, risk control, etc.) and uses LLMs to generate and evaluate allocation plans;
• Risk Management Agent: Monitors indicators such as volatility and maximum drawdown, proposes adjustment suggestions, or triggers automatic rebalancing.

The project uses Python as the main development language and leverages the data science ecosystem. It features a modular and extensible design, with agents communicating via interfaces, supporting replacement of LLM backends or addition of new agents. Installation and deployment are straightforward—using virtual environments and dependency files to quickly set up the runtime environment, facilitating community contributions and promotion.

The value of AlphaAgents is reflected in multiple aspects:

• Individual investors: A low-cost, professional-grade analysis tool;
• Quantitative teams: An extensible experimental platform;
• Educational institutions: A demonstration of AI application paradigms in the financial field. The core is a human-machine collaborative decision-making model, where LLMs act as intelligent assistants to enhance human decision-making while retaining human strategic judgment and exception-handling capabilities.

Limitations: The financial market is complex and non-stationary, so the model cannot guarantee sustained profitability; LLM outputs have uncertainties, requiring strict risk control; engineering details such as real-time data and transaction costs need to be considered. Outlook: With the development of LLM and multi-agent technologies, the system's functions and decision-making quality will improve, and it is expected to become a more intelligent and personalized investment assistant, adapting to a wider range of market environments.

AlphaAgents is an important milestone for the open-source community in the field of financial AI. It proves the feasibility of combining cutting-edge AI technology with traditional financial theory, provides a reference for subsequent research and applications, and is an excellent project worth paying attention to for the development of intelligent investment systems.